Information Bundling and Polarizing Persuasion

Last registered on July 02, 2024

View Trial History

Pre-Trial

Trial Information

General Information

Title

Information Bundling and Polarizing Persuasion

RCT ID

AEARCTR-0012729

Initial registration date

January 29, 2024

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

January 31, 2024, 12:14 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated

July 02, 2024, 6:33 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country

United States of America

Region

Primary Investigator

Name

Giampaolo Bonomi

Affiliation

University of California San Diego (UCSD)

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

In development

Start date

2024-02-15

End date

2024-09-01

Keywords

Behavior, Electoral

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

We study how bundling together policy recommendations on different policy issues affects voters' policy views. Voters are randomized into (i) treatment messages, each consisting of policy recommendations on two policy issues, and (ii) control messages, where policy recommendations are sent separately. The issues bundled differ in ideological value, policy domain, and complexity. We investigate the presence of belief spillovers across policy domains, and the role played by trust and identity in explaining these spillovers.

External Link(s)

Registration Citation

Citation

Bonomi, Giampaolo. 2024. "Information Bundling and Polarizing Persuasion." AEA RCT Registry. July 02. https://doi.org/10.1257/rct.12729-1.2

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Experimental Details

Interventions

Intervention(s)

Intervention (Hidden)

Intervention Start Date

2024-07-02

Intervention End Date

2024-08-04

Primary Outcomes

Primary Outcomes (end points)

Policy preferences on economic policy issues (trade policy, healthcare regulations, and redistributive policies) and social policy issues (abortion policy, affirmative action, gun rights, etc)

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

The experiment is implemented online on Amazon Mechanical Turk via Cloud Research. Before randomization, we collect the socio-demographic characteristics of respondents, as well as their perceived importance and knowledge of the policy issues used as main outcomes. Participants are then randomized into (i) treatment messages, each consisting of written policy recommendation on two policy issues, and (ii) control messages, where the same policy recommendations are reported separately (respondents observe one recommendation at a time). The issues bundled differ in ideological value, policy domain, and complexity. Before and after reading the messages, respondents are asked to provide their policy preference for the two issues (in the main experiment, one preference is asked before treatment while the other is asked after treatment).

Experimental Design Details

Randomization Method

Randomization performed through Qualtrics' block randomization option.

Randomization Unit

Individual randomization

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

12,000

Sample size: planned number of observations

12,000

Sample size (or number of clusters) by treatment arms

For each pair of proposals on issues X and Y, an equal number of respondents will be allocated to the following three conditions: (i) X and Y made by the same source (ii) X and Y sent separately (i.e., not sent together by the same source). (iii) no message shown. Subjects will be assigned evenly to conditions. We plan to assign 600 subjects to each condition.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB